The coordinates of the Legionnairess Disease outbreaks will be used to gather weather data from surrounding stations. The averages of the data will be taken and outputted into a graph containing data from the last 10 years before the outbreak.

library(devtools)
library(rnoaa)
library(countyweather)
library(dplyr)
library(plyr)
library(tidyr)
library(weathermetrics)
library(ggplot2)
library(lubridate)
library(knitr)

I created a data frame including the locations of each outbreak. I found the coordinates at http://maps.cga.harvard.edu/gpf/ and crossed checked them with Google coordinates. The other data in this set are year of outbreak and the year 10 years before the outbreak, onset date, and 14 days before the onset date.

id	file_id	latitude	longitude	year_min	date_min	year_max	date_max	onset	before_onset
portugal	portugal	38.96	-8.99	2004	2004-01-01	2014	2014-12-31	2004-10-14	2004-09-30
pittsburgh	pittsburgh	40.43	-79.98	2002	2002-01-01	2012	2012-12-31	2012-08-26	2012-08-12
quebec	quebec	46.85	-71.34	2002	2002-01-01	2012	2012-12-31	2012-07-18	2012-07-04
stoke-on-trent	stoke_on_trent	53.02	-2.15	2002	2002-01-01	2012	2012-12-31	2012-07-02	2012-06-18
edinburgh	edinburgh	55.94	-3.20	2002	2002-01-01	2012	2012-12-31	2012-05-01	2012-04-17
miyazaki	miyazaki	31.89	131.34	1992	1992-01-01	2002	2002-12-31	2002-07-18	2002-07-04
pas-de-calais	pas_de_calais	50.51	2.37	1993	1993-01-01	2003	2003-12-31	2003-11-28	2003-11-14
pamplona	pamplona	42.81	-1.65	1996	1996-01-01	2006	2006-12-31	2006-06-01	2006-05-18
rapid city	rapid_city	44.06	-103.22	1995	1995-01-01	2005	2005-12-31	2005-05-26	2005-05-12
sarpsborg	sarpsborg	59.28	11.08	1995	1995-01-01	2005	2005-12-31	2005-05-12	2005-04-28
barrow-in-furness	barrow_in_furness	54.10	-3.22	1992	1992-01-01	2002	2002-12-31	2002-07-30	2002-07-16
murcia	murcia	37.98	-1.12	1991	1991-01-01	2001	2001-12-31	2001-06-26	2001-06-12
melbourne	melbourne	-37.86	145.07	1990	1990-01-01	2000	2000-12-31	2000-04-17	2000-04-03
bovenkarspel	bovenkarspel	52.70	5.24	1989	1989-01-01	1999	1999-12-31	1999-02-25	1999-02-11
london	london	51.52	-0.10	1979	1979-01-01	1989	1989-12-31	1989-01-01	1988-12-18
sydney	sydney	-33.85	150.93	2006	2006-01-01	2016	2016-12-31	2016-04-25	2016-04-11
genesee1	genesee1	43.09	-83.63	2004	2004-01-01	2014	2014-12-31	2014-06-06	2014-05-23
genesee2	genesee2	43.09	-83.63	2005	2005-01-01	2015	2015-12-31	2015-05-04	2015-04-20
columbus	columbus	39.98	-82.99	2003	2003-01-01	2013	2013-12-31	2013-07-09	2013-06-25
bronx	bronx	40.82	-73.92	2005	2005-01-01	2015	2015-12-31	2015-07-12	2015-06-28

The next function will download information from all of the stations. It only needs to be downloaded once per session. It will take a couple minutes to download.

I created a loop to get a list of the stations within 30 km of the location.

station_data <- ghcnd_stations()[[1]]
df <- list()
for(i in 1:length(outbreak_loc$id))
  {
    df[[i]] <- (meteo_nearby_stations(lat_lon_df = outbreak_loc[i,],
                                    station_data = station_data,
                                    var = c("PRCP","TAVG","TMAX","TMIN",
                                            "AWND","MDPR"),
                                    year_min = outbreak_loc[i, "year_min"],
                                    year_max = outbreak_loc[i, "year_max"],
                                    radius = 30)[[1]])
  }

names(df) <- outbreak_loc$id
stations <- df
saveRDS(stations, file = "stations.RData")

## $portugal
## [1] id        name      latitude  longitude distance 
## <0 rows> (or 0-length row.names)
## 
## $pittsburgh
##             id                          name latitude longitude  distance
## 1  US1PAAL0014          PA PITTSBURGH 1.6 SW  40.4226  -79.9974  1.687108
## 2  US1PAAL0017           PA WHITEHALL 1.0 SW  40.3475  -80.0022  9.364279
## 3  USW00014762 PA PITTSBURGH ALLEGHENY CO AP  40.3547  -79.9217  9.720301
## 4  US1PAAL0011        PA WEST MIFFLIN 1.3 SW  40.3466  -79.9283 10.255413
## 5  US1PAAL0031      PA SCOTT TOWNSHIP 1.3 NW  40.3978  -80.0967 10.508788
## 6  USC00360861            PA BRADDOCK LOCK 2  40.3917  -79.8594 11.063215
## 7  US1PAAL0009                 PA PATHFINDER  40.3416  -80.0485 11.414110
## 8  USC00362574      PA EMSWORTH L/D OHIO RVR  40.5019  -80.0833 11.844197
## 9  US1PAAL0016            PA GLENSHAW 1.3 NW  40.5488  -79.9800 13.209957
## 10 USC00365573                 PA MCKEESPORT  40.3392  -79.8603 14.308275
## 11 US1PAAL0008    PA UPPER ST. CLAIR 1.7 WNW  40.3412  -80.1026 14.329100
## 12 US1PAAL0020         PA ALLISON PARK 0.7 W  40.5610  -79.9708 14.587294
## 13 US1PAAL0023 PA SOUTH PARK TOWNSHIP 0.2 NW  40.2989  -79.9970 14.648635
## 14 US1PAAL0003         PA SOUTH FAYETTE 2 SE  40.3381  -80.1159 15.392161
## 15 US1PAAL0001         PA BRIDGEVILLE 1.4 SW  40.3417  -80.1229 15.584945
## 16 US1PAAL0004           PA PENN HILLS 1.5 E  40.4759  -79.7982 16.207171
## 17 USC00360022           PA ACMETONIA LOCK 3  40.5361  -79.8153 18.254228
## 18 US1PAAL0006           PA MCDONALD 2.5 ENE  40.3822  -80.1871 18.323293
## 19 US1PAWS0005            PA MCMURRAY 0.2 NE  40.2831  -80.0857 18.628831
## 20 USW00094823         PA PITTSBURGH INTL AP  40.4847  -80.2144 20.743637
## 21 US1PAAL0030          PA CARNOT-MOON 0.9 S  40.5061  -80.2119 21.364459
## 22 USC00366111           PA MURRYSVILLE 2 SW  40.4119  -79.7244 21.730660
## 23 US1PAWT0001        PA NORTH IRWIN 2.5 WSW  40.3243  -79.7556 22.348628
## 24 USC00365918              PA MOON TOWNSHIP  40.5319  -80.2172 23.040372
## 25 USC00363343      PA GLENWILLARD DASHIELDS  40.5514  -80.2167 24.143027
## 26 US1PAWT0010        PA MURRYSVILLE 1.5 WSW  40.4317  -79.6813 25.282776
## 27 US1PAAL0012        PA SOUTH HEIGHTS 1.5 S  40.5533  -80.2379 25.760534
## 
## $quebec
##            id                         name latitude longitude  distance
## 1 CA007011309  QC CHARLESBOURG PARC ORLEAN  46.8667  -71.2667  5.874619
## 2 CA007016294 QC QUEBEC/JEAN LESAGE INTL A  46.8000  -71.3833  6.462488
## 3 CA00701S001   QC QUEBEC/JEAN LESAGE INTL  46.8000  -71.3833  6.462488
## 4 CA00701Q004        QC STE-FOY (U. LAVAL)  46.7833  -71.2833  8.580380
## 5 CA007010565                  QC BEAUPORT  46.8333  -71.2000 10.808994
## 6 CA007018572                QC VALCARTIER  46.9000  -71.5000 13.372471
## 7 CA007024254                    QC LAUZON  46.8167  -71.1000 18.628731
## 8 CA007020567                QC BEAUSEJOUR  46.6667  -71.1667 24.283856
## 9 CA007041330            QC CHATEAU RICHER  46.9667  -71.0333 26.668362
## 
## $`stoke-on-trent`
## [1] id        name      latitude  longitude distance 
## <0 rows> (or 0-length row.names)
## 
## $edinburgh
## [1] id        name      latitude  longitude distance 
## <0 rows> (or 0-length row.names)
## 
## $miyazaki
##            id       name latitude longitude  distance
## 1 JA000047830   MIYAZAKI   31.933   131.417  8.699733
## 2 JA000047829 MIYAKONOJO   31.733   131.083 29.908209
## 
## $`pas-de-calais`
## [1] id        name      latitude  longitude distance 
## <0 rows> (or 0-length row.names)
## 
## $pamplona
##            id                    name latitude longitude distance
## 1 SPE00120350 PAMPLONA (OBSERVATORIO)  42.8175   -1.6364 1.387848
## 2 SPE00120359                PAMPLONA  42.7767   -1.6500 3.702791
## 
## $`rapid city`
##             id                         name latitude longitude  distance
## 1  USC00396948            SD RAPID CITY WFO  44.0728 -103.2108  1.601898
## 2  USC00396947            SD RAPID CITY 4NW  44.1150 -103.2828  7.909484
## 3  USW00024090        SD RAPID CITY RGNL AP  44.0433 -103.0536 13.427258
## 4  USC00394343            SD JOHNSON SIDING  44.0839 -103.4342 17.317536
## 5  USR0000SBAK   SD BAKER PARK SOUTH DAKOTA  43.9792 -103.4250 18.692682
## 6  USC00396427               SD PACTOLA DAM  44.0622 -103.4819 20.928415
## 7  USC00394556                  SD KEYSTONE  43.9039 -103.4100 23.073539
## 8  USR0000SNEM         SD NEMO SOUTH DAKOTA  44.1917 -103.5097 27.370234
## 9  USC00395870      SD MT RUSHMORE NATL MEM  43.8769 -103.4578 27.869317
## 10 USR0000SMRU SD MT. RUSHMORE SOUTH DAKOTA  43.8750 -103.4583 28.051427
## 11 USC00393775             SD HERMOSA 3 SSW  43.8069 -103.2131 28.148859
## 
## $sarpsborg
##            id              name latitude longitude  distance
## 1 NOE00109849         SARPSBORG  59.2856   11.1144  2.050694
## 2 NOE00134298            FLOTER  59.4964   11.0131 24.358920
## 3 NOE00100575            HALDEN  59.1225   11.3883 24.795447
## 4 NOE00109786            HVALER  59.0358   11.0517 27.201683
## 5 NOE00109876 MOSS BRANNSTASJON  59.4428   10.6842 28.822789
## 6 NOE00109867              MOSS  59.4339   10.6667 29.008867
## 
## $`barrow-in-furness`
## [1] id        name      latitude  longitude distance 
## <0 rows> (or 0-length row.names)
## 
## $murcia
##            id                name latitude longitude distance
## 1 SPE00120323              MURCIA  38.0028   -1.1692 5.001690
## 2 SPE00120332 MURCIA/ALCANTARILLA  37.9578   -1.2294 9.902609
## 
## $melbourne
##             id                           name latitude longitude  distance
## 1  ASN00086018         CAULFIELD (RACECOURSE) -37.8795  145.0368  3.632396
## 2  ASN00086304      HAWTHORN (SCOTCH COLLEGE) -37.8361  145.0294  4.446429
## 3  ASN00086095           PRAHRAN (COMO HOUSE) -37.8376  145.0048  6.243145
## 4  ASN00086088 OAKLEIGH (METROPOLITAN GOLF CL -37.9142  145.0935  6.369850
## 5  ASN00086012     BOX HILL AGED MENS RETREAT -37.8364  145.1364  6.393542
## 6  ASN00086006                      BENTLEIGH -37.9279  145.0749  7.562369
## 7  ASN00086033 BRIGHTON (DENDY PARK BOWLING C -37.9252  145.0254  8.238821
## 8  ASN00086232    MELBOURNE BOTANICAL GARDENS -37.8303  144.9767  8.833034
## 9  ASN00086279                      NORTHCOTE -37.7797  145.0314  9.551015
## 10 ASN00086316     VERMONT TRANSPORT RESEARCH -37.8587  145.1847 10.070617
## 11 ASN00086071      MELBOURNE REGIONAL OFFICE -37.8075  144.9700 10.545355
## 12 ASN00086020     CHELTENHAM KINGSTON CENTRE -37.9551  145.0782 10.599081
## 13 ASN00086303    GLEN WAVERLEY (GOLF COURSE) -37.8886  145.1928 11.237859
## 14 ASN00086074                        MITCHAM -37.8219  145.1906 11.406147
## 15 ASN00086260                HEIDELBERG MMBW -37.7567  145.0533 11.579751
## 16 ASN00086378                      BRUNSWICK -37.7667  144.9797 13.059611
## 17 ASN00086111          SPRINGVALE NECROPOLIS -37.9445  145.1764 13.245203
## 18 ASN00086369           SPRINGVALE (SANDOWN) -37.9535  145.1655 13.352636
## 19 ASN00086068             VIEWBANK (ARPANSA) -37.7408  145.0972 13.468157
## 20 ASN00086077              MOORABBIN AIRPORT -37.9800  145.0964 13.542852
## 21 ASN00086146                      BEAUMARIS -37.9771  145.0273 13.548961
## 22 ASN00086362   DONCASTER (MANNINGHAM DEPOT) -37.7494  145.1703 15.129262
## 23 ASN00086351  BUNDOORA (LATROBE UNIVERSITY) -37.7163  145.0453 16.125457
## 24 ASN00086039          FLEMINGTON RACECOURSE -37.7915  144.9067 16.239778
## 25 ASN00086104    SCORESBY RESEARCH INSTITUTE -37.8710  145.2561 16.382188
## 26 ASN00086096              PRESTON RESERVOIR -37.7214  145.0059 16.408663
## 27 ASN00086230                      BAYSWATER -37.8372  145.2558 16.509685
## 28 ASN00086379                 RINGWOOD NORTH -37.7917  145.2433 17.010511
## 29 ASN00086101                       RINGWOOD -37.8000  145.2500 17.158761
## 30 ASN00086313                     WARRANDYTE -37.7469  145.2098 17.578887
## 31 ASN00086347       YARRA RIVER @ WARRANDYTE -37.7417  145.2167 18.416453
## 32 ASN00086224                      DANDENONG -37.9785  145.2235 18.839479
## 33 ASN00086035                         ELTHAM -37.7011  145.1547 19.172878
## 34 ASN00087038 MARIBYRNONG EXPLOSIVES FACTORY -37.7750  144.8767 19.432886
## 35 ASN00086324       FERNTREE GULLY (PROBERT) -37.8797  145.2964 19.993316
## 36 ASN00086027        CROYDON (SAMUEL STREET) -37.7903  145.2812 20.103933
## 37 ASN00086234        CROYDON (COUNCIL DEPOT) -37.7869  145.2847 20.535018
## 38 ASN00086038               ESSENDON AIRPORT -37.7276  144.9066 20.564229
## 39 ASN00087131          ALTONA (CITY OFFICES) -37.8633  144.8261 21.414594
## 40 ASN00087148    SUNSHINE (CITY OF BRINBANK) -37.7928  144.8344 22.000521
## 41 ASN00086250                         PLENTY -37.6600  145.1244 22.747356
## 42 ASN00086256                    FERNY CREEK -37.8833  145.3333 23.256162
## 43 ASN00086210              BONBEACH (CARRUM) -38.0651  145.1294 23.393049
## 44 ASN00086372       FERNY CREEK (DUNNS HILL) -37.8775  145.3364 23.465245
## 45 ASN00086365                    MOOROOLBARK -37.7792  145.3197 23.701976
## 46 ASN00086251            UPWEY SHIRE COUNCIL -37.9144  145.3317 23.749365
## 47 ASN00086243           MOUNT DANDENONG GTV9 -37.8306  145.3500 24.802430
## 48 ASN00086059                KANGAROO GROUND -37.6830  145.2518 25.351527
## 49 ASN00086254       CARRUM DOWNS SEWER WORKS -38.0783  145.1733 25.907856
## 50 ASN00086036                         EPPING -37.6312  144.9846 26.526359
## 51 ASN00086066                       LILYDALE -37.7488  145.3416 26.875070
## 52 ASN00086076                       MONTROSE -37.8019  145.3675 26.914609
## 53 ASN00086085 NARRE WARREN NORTH (NARRE WARR -37.9897  145.3356 27.399212
## 54 ASN00087031                  LAVERTON RAAF -37.8565  144.7566 27.516720
## 55 ASN00087027               KEILOR (ARUNDEL) -37.6942  144.8342 27.737658
## 56 ASN00087177            LAVERTON COMPARISON -37.8633  144.7456 28.480727
## 57 ASN00086305            GREENVALE RESERVOIR -37.6369  144.9072 28.640881
## 58 ASN00086384   MELBOURNE AIRPORT COMPARISON -37.6750  144.8419 28.725743
## 59 ASN00087015                         KEILOR -37.7025  144.8072 28.984935
## 
## $bovenkarspel
##             id          name latitude longitude  distance
## 1  NLE00101917     ENKHUIZEN  52.6917    5.2944  3.780363
## 2  NLE00109144   HOOGKARSPEL  52.6867    5.1669  5.143626
## 3  NLE00101928     MEDEMBLIK  52.7781    5.1014 12.746887
## 4  NLE00100501         HOORN  52.6444    5.0681 13.136277
## 5  NLE00102479      BERKHOUT  52.6428    4.9789 18.718869
## 6  NLE00109146      HOOGWOUD  52.7281    4.9608 19.065006
## 7  NLE00109174   KREILEROORD  52.8619    5.0953 20.464698
## 8  NLE00102134      STAVOREN  52.8967    5.3831 23.894441
## 9  NLE00109232         OBDAM  52.6775    4.8769 24.600539
## 10 NLE00109054          EDAM  52.5114    5.0467 24.701897
## 11 NLE00109250    OUDEMIRDUM  52.8608    5.5078 25.379621
## 12 NLE00109162       KOLHORN  52.7914    4.8919 25.540549
## 13 NLE00109354 WEST BEEMSTER  52.5817    4.9028 26.281190
## 14 NLE00101948     TOLLEBEEK  52.6719    5.6300 26.472774
## 15 NLE00101930     DEN OEVER  52.9217    5.0383 28.133541
## 16 NLE00101932        MARKEN  52.4600    5.1078 28.142012
## 17 NLE00109028     DE HAUKES  52.8783    4.9408 28.246661
## 18 NLE00109254     PURMEREND  52.5125    4.9506 28.575998
## 
## $london
##            id     name latitude longitude distance
## 1 UKM00003772 HEATHROW   51.478    -0.461 25.42177
## 
## $sydney
##             id                           name latitude longitude  distance
## 1  ASN00067019             PROSPECT RESERVOIR -33.8193  150.9127  3.769152
## 2  ASN00067017   GREYSTANES (BATHURST STREET) -33.8136  150.9392  4.135739
## 3  ASN00067070   MERRYLANDS (WELSFORD STREET) -33.8269  150.9767  5.020100
## 4  ASN00067114 ABBOTSBURY (FAIRFIELD CITY FAR -33.8667  150.8611  6.627566
## 5  ASN00067119 HORSLEY PARK EQUESTRIAN CENTRE -33.8511  150.8567  6.770114
## 6  ASN00067110  SEVEN HILLS  (RADIO FM 103.2) -33.7858  150.9236  7.163157
## 7  ASN00067026       SEVEN HILLS (COLLINS ST) -33.7704  150.9318  8.852678
## 8  ASN00067020 LIVERPOOL (MICHAEL WENDEN CENT -33.9214  150.8861  8.913714
## 9  ASN00066137          BANKSTOWN AIRPORT AWS -33.9181  150.9864  9.189477
## 10 ASN00066134       GRANVILLE SHELL REFINERY -33.8322  151.0340  9.806921
## 11 ASN00066168 MILPERRA BRIDGE (GEORGES RIVER -33.9289  150.9831 10.049571
## 12 ASN00067042    KINGS LANGLEY (SOLANDER RD) -33.7610  150.9498 10.064021
## 13 ASN00067111 NORTH PARRAMATTA (BURNSIDE HOM -33.7931  151.0167 10.206744
## 14 ASN00067109   BAULKHAM HILLS EUCALYPTUS CT -33.7678  150.9814 10.300292
## 15 ASN00066124 PARRAMATTA NORTH (MASONS DRIVE -33.7917  151.0181 10.404864
## 16 ASN00066050           POTTS HILL RESERVOIR -33.8933  151.0346 10.790772
## 17 ASN00066164       ROOKWOOD (HAWTHORNE AVE) -33.8771  151.0577 12.169844
## 18 ASN00067112 NORTH ROCKS (MUIRFIELD GOLF CL -33.7672  151.0186 12.319787
## 19 ASN00066195 SYDNEY OLYMPIC PARK (SYDNEY OL -33.8521  151.0646 12.431978
## 20 ASN00066070          STRATHFIELD GOLF CLUB -33.8805  151.0631 12.748603
## 21 ASN00066054         REVESBY (PATEN STREET) -33.9474  151.0065 12.928587
## 22 ASN00067076   QUAKERS HILL TREATMENT WORKS -33.7366  150.8758 13.567795
## 23 ASN00066185      CARLINGFORD (BARELLAN AV) -33.7801  151.0587 14.205035
## 24 ASN00066191        GLENFIELD (HARROW ROAD) -33.9770  150.9042 14.321038
## 25 ASN00067117       HOLSWORTHY CONTROL RANGE -33.9795  150.9254 14.405998
## 26 ASN00067102          ST CLAIR (JUBA CLOSE) -33.8044  150.7778 14.945410
## 27 ASN00067100     CASTLE HILL (KATHLEEN AVE) -33.7260  150.9944 15.017779
## 28 ASN00067089 WEST PENNANT HILLS (CUMBERLAND -33.7459  151.0402 15.416884
## 29 ASN00067003         COLYTON (CARPENTER ST) -33.7770  150.7877 15.450666
## 30 ASN00067098 WEST PENNANT HILLS  (ORATAVA A -33.7487  151.0449 15.478987
## 31 ASN00066013              CONCORD GOLF CLUB -33.8523  151.0985 15.562401
## 32 ASN00067061         ROSSMORE (SOUTH CREEK) -33.9353  150.7819 16.638119
## 33 ASN00066048             CONCORD (BRAYS RD) -33.8483  151.1105 16.669913
## 34 ASN00067037         SCHOFIELDS BOUNDARY RD -33.6947  150.8868 17.724215
## 35 ASN00066194      CANTERBURY RACECOURSE AWS -33.9057  151.1134 18.028240
## 36 ASN00066148            PEAKHURST GOLF CLUB -33.9700  151.0638 18.179759
## 37 ASN00066034 ABBOTSFORD (BLACKWALL POINT RD -33.8507  151.1295 18.423361
## 38 ASN00067116       WILLMOT (RESOLUTION AVE) -33.7231  150.7997 18.550317
## 39 ASN00066156 MACQUARIE PARK (WILLANDRA VILL -33.7791  151.1121 18.579019
## 40 ASN00066047   PENNANT HILLS (YARRARA ROAD) -33.7324  151.0767 18.835558
## 41 ASN00066190   INGLEBURN (SACKVILLE STREET) -34.0117  150.8647 18.962689
## 42 ASN00067108             BADGERYS CREEK AWS -33.8969  150.7281 19.355573
## 43 ASN00066181       OATLEY (WORONORA PARADE) -33.9766  151.0766 19.523783
## 44 ASN00066004            BEXLEY BOWLING CLUB -33.9430  151.1098 19.553323
## 45 ASN00067086      DURAL (OLD NORTHERN ROAD) -33.6867  151.0250 20.170027
## 46 ASN00066036         MARRICKVILLE GOLF CLUB -33.9186  151.1402 20.849104
## 47 ASN00066131          RIVERVIEW OBSERVATORY -33.8258  151.1556 21.009524
## 48 ASN00067104          BOX HILL (HYNDS ROAD) -33.6617  150.9000 21.120894
## 49 ASN00066189       WEST PYMBLE (WYUNA ROAD) -33.7693  151.1380 21.209116
## 50 ASN00067084  ORCHARD HILLS TREATMENT WORKS -33.8020  150.7069 21.288391
## 51 ASN00066204  OYSTER BAY (GREEN POINT ROAD) -34.0009  151.0738 21.391107
## 52 ASN00066158 TURRAMURRA (KISSING POINT ROAD -33.7366  151.1271 22.152621
## 53 ASN00066120               GORDON GOLF CLUB -33.7617  151.1462 22.258367
## 54 ASN00066078          LUCAS HEIGHTS (ANSTO) -34.0517  150.9800 22.897282
## 55 ASN00067015           BRINGELLY (MARYLAND) -33.9696  150.7250 23.124628
## 56 ASN00068160 CAMPBELLTOWN (KENTLYN (GEORGES -34.0542  150.8772 23.222412
## 57 ASN00068250      CAMDEN VALLEY GOLF RESORT -34.0128  150.7675 23.504569
## 58 ASN00066157      PYMBLE (CANISIUS COLLEGE) -33.7371  151.1521 24.058869
## 59 ASN00066058     SANS SOUCI (PUBLIC SCHOOL) -33.9942  151.1292 24.391064
## 60 ASN00067022      GALSTON (ROWLAND VILLAGE) -33.6550  151.0553 24.583503
## 61 ASN00068231          RUSE (DENISON STREET) -34.0630  150.8489 24.837610
## 62 ASN00066114   NORTH TURRAMURRA (DRYDEN RD) -33.7179  151.1470 24.858762
## 63 ASN00066037             SYDNEY AIRPORT AMO -33.9465  151.1731 24.870760
## 64 ASN00066062      SYDNEY (OBSERVATORY HILL) -33.8607  151.2050 25.421751
## 65 ASN00066011         CHATSWOOD BOWLING CLUB -33.8000  151.2000 25.553205
## 66 ASN00067115 GLENMORE PARK  (CARTWRIGHT CL) -33.7826  150.6619 25.877100
## 67 ASN00066006         SYDNEY BOTANIC GARDENS -33.8662  151.2160 26.470162
## 68 ASN00066080   CASTLE COVE (ROSEBRIDGE AVE) -33.7809  151.2044 26.489163
## 69 ASN00066176  AUDLEY  (ROYAL NATIONAL PARK) -34.0658  151.0567 26.689966
## 70 ASN00067029           WALLACIA POST OFFICE -33.8637  150.6410 26.729649
## 71 ASN00066206      ST IVES (RICHMOND AVENUE) -33.7096  151.1730 27.351861
## 72 ASN00067113              PENRITH LAKES AWS -33.7195  150.6783 27.416523
## 73 ASN00068257     CAMPBELLTOWN (MOUNT ANNAN) -34.0615  150.7735 27.594122
## 74 ASN00066073            RANDWICK RACECOURSE -33.9105  151.2276 28.284461
## 75 ASN00068254     MOUNT ANNAN BOTANIC GARDEN -34.0673  150.7678 28.418732
## 76 ASN00066160                CENTENNIAL PARK -33.8959  151.2341 28.535385
## 77 ASN00067031           WINDSOR BOWLING CLUB -33.6100  150.8151 28.724326
## 78 ASN00066188         BELROSE (EVELYN PLACE) -33.7402  151.2173 29.221247
## 79 ASN00066052          RANDWICK BOWLING CLUB -33.9096  151.2419 29.545881
## 80 ASN00066086                   CRONULLA STP -34.0313  151.1642 29.549535
## 81 ASN00067010     GLENORIE (OLD NORTHERN RD) -33.5908  151.0094 29.742533
## 
## $genesee1
##             id                    name latitude longitude  distance
## 1  USC00201150            MI BURTON 4N  43.0675  -83.5919  3.979309
## 2  US1MIGN0010       MI BURTON 0.9 NNW  43.0085  -83.6274  9.064849
## 3  US1MIGN0008 MI MOUNT MORRIS 3.1 WSW  43.1057  -83.7580 10.538333
## 4  US1MIGN0014       MI DAVISON 3.3 SW  43.0003  -83.5684 11.159857
## 5  US1MIGN0005      MI DAVISON 0.7 SSW  43.0219  -83.5246 11.431375
## 6  USC00202851            MI FLINT 7 W  43.0378  -83.7694 12.725462
## 7  USC00201645                 MI CLIO  43.1794  -83.7369 13.193328
## 8  US1MIGN0009        MI BURTON 3.3 SW  42.9613  -83.6636 14.569099
## 9  USW00014826 MI FLINT BISHOP INTL AP  42.9667  -83.7494 16.797891
## 10 US1MIGN0023  MI GRAND BLANC 3.8 WNW  42.9440  -83.6886 16.919078
## 11 US1MISG0004    MI BIRCH RUN 2.6 ESE  43.2291  -83.7470 18.146491
## 12 US1MIGN0020 MI SWARTZ CREEK 2.0 NNE  42.9897  -83.8166 18.824526
## 13 US1MIGN0015        MI FLINT 6.4 SSW  42.9326  -83.7210 19.001794
## 14 US1MIGN0018   MI GRAND BLANC 0.7 SE  42.9187  -83.6079 19.132279
## 15 USC00203278             MI GOODRICH  42.9164  -83.5097 21.640755
## 16 USC00204659            MI LAPEER 2W  43.0581  -83.3606 22.167570
## 17 US1MIGN0022   MI GRAND BLANC 2.9 SE  42.8909  -83.5858 22.428899
## 18 US1MIGN0004      MI MONTROSE 0.4 NW  43.1794  -83.8987 23.962697
## 19 USC00205488      MI MILLINGTON 3 SE  43.2836  -83.4792 24.756898
## 20 US1MILP0003       MI LAPEER 1.1 SSW  43.0316  -83.3293 25.277892
## 21 USC00204655          MI LAPEER WWTP  43.0608  -83.3075 26.394851
## 22 USC00202955      MI FRANKENMUTH 1SE  43.3194  -83.7161 26.445482
## 
## $genesee2
##             id                    name latitude longitude  distance
## 1  USC00201150            MI BURTON 4N  43.0675  -83.5919  3.979309
## 2  US1MIGN0010       MI BURTON 0.9 NNW  43.0085  -83.6274  9.064849
## 3  US1MIGN0008 MI MOUNT MORRIS 3.1 WSW  43.1057  -83.7580 10.538333
## 4  US1MIGN0014       MI DAVISON 3.3 SW  43.0003  -83.5684 11.159857
## 5  US1MIGN0005      MI DAVISON 0.7 SSW  43.0219  -83.5246 11.431375
## 6  USC00202851            MI FLINT 7 W  43.0378  -83.7694 12.725462
## 7  US1MIGN0024          MI CLIO 0.4 SW  43.1725  -83.7423 12.930649
## 8  USC00201645                 MI CLIO  43.1794  -83.7369 13.193328
## 9  US1MIGN0009        MI BURTON 3.3 SW  42.9613  -83.6636 14.569099
## 10 USW00014826 MI FLINT BISHOP INTL AP  42.9667  -83.7494 16.797891
## 11 US1MIGN0023  MI GRAND BLANC 3.8 WNW  42.9440  -83.6886 16.919078
## 12 US1MISG0004    MI BIRCH RUN 2.6 ESE  43.2291  -83.7470 18.146491
## 13 US1MIGN0020 MI SWARTZ CREEK 2.0 NNE  42.9897  -83.8166 18.824526
## 14 US1MIGN0015        MI FLINT 6.4 SSW  42.9326  -83.7210 19.001794
## 15 US1MIGN0018   MI GRAND BLANC 0.7 SE  42.9187  -83.6079 19.132279
## 16 USC00203278             MI GOODRICH  42.9164  -83.5097 21.640755
## 17 USC00204659            MI LAPEER 2W  43.0581  -83.3606 22.167570
## 18 US1MIGN0022   MI GRAND BLANC 2.9 SE  42.8909  -83.5858 22.428899
## 19 US1MIGN0004      MI MONTROSE 0.4 NW  43.1794  -83.8987 23.962697
## 20 USC00205488      MI MILLINGTON 3 SE  43.2836  -83.4792 24.756898
## 21 US1MILP0003       MI LAPEER 1.1 SSW  43.0316  -83.3293 25.277892
## 22 USC00204655          MI LAPEER WWTP  43.0608  -83.3075 26.394851
## 23 USC00202955      MI FRANKENMUTH 1SE  43.3194  -83.7161 26.445482
## 
## $columbus
##             id                              name latitude longitude
## 1  US1OHFR0018               OH COLUMBUS 2.4 WNW  39.9977  -83.0323
## 2  US1OHFR0025               OH COLUMBUS 2.8 WSW  39.9804  -83.0397
## 3  US1OHFR0003        OH GRANDVIEW HEIGHTS 0.1 N  39.9810  -83.0401
## 4  USC00331785                  OH COLUMBUS WCMH  40.0250  -83.0269
## 5  US1OHFR0034                OH COLUMBUS 3.6 NW  40.0191  -83.0437
## 6  US1OHFR0020                OH COLUMBUS 3.5 NE  40.0287  -82.9477
## 7  US1OHFR0001          OH UPPER ARLINGTON 0.9 E  40.0279  -83.0543
## 8  US1OHFR0021           OH MARBLE CLIFF 1.1 WNW  39.9931  -83.0786
## 9  US1OHFR0007        OH UPPER ARLINGTON 1.3 SSW  40.0112  -83.0832
## 10 USW00014821 OH COLUMBUS PORT COLUMBUS INTL AP  39.9914  -82.8808
## 11 USC00331783       OH COLUMBUS-VALLEY CROSSING  39.9047  -82.9200
## 12 US1OHFR0012        OH UPPER ARLINGTON 2.4 NNW  40.0604  -83.0815
## 13 USC00331777        OH COLUMBUS-HAP CREMEAN WP  40.0603  -82.8942
## 14 US1OHFR0024               OH COLUMBUS 9.3 NNE  40.0925  -82.9582
## 15 USW00004804    OH COLUMBUS OHIO STATE UNIV AP  40.0781  -83.0781
## 16 US1OHFR0037             OH REYNOLDSBURG 1.6 W  39.9588  -82.8294
## 17 US1OHFR0016                 OH DUBLIN 3.7 ESE  40.0923  -83.0725
## 18 US1OHFR0022                 OH GALLOWAY 3.1 N  39.9561  -83.1592
## 19 USC00331779          OH COLUMBUS-PARSONS AVE.  39.8469  -82.9872
## 20 USC00338951                    OH WESTERVILLE  40.1264  -82.9433
## 21 US1OHFR0010            OH WESTERVILLE 0.2 WNW  40.1226  -82.9213
## 22 US1OHFR0030                 OH HILLIARD 1.8 W  40.0344  -83.1768
## 23 US1OHFR0008             OH NEW ALBANY 2.8 SSE  40.0403  -82.7980
## 24 US1OHFR0002                 OH DUBLIN 3.2 ENE  40.1299  -83.0742
## 25 US1OHLC0002              OH PATASKALA 4.4 WNW  40.0273  -82.7490
## 26 US1OHFF0005           OH PICKERINGTON 2.7 NNE  39.9263  -82.7469
## 27 US1OHDL0002              OH WESTERVILLE 4.0 N  40.1790  -82.9256
## 28 US1OHFR0023             OH HARRISBURG 3.7 WNW  39.8378  -83.2321
## 29 US1OHLC0011               OH PATASKALA 2.0 NE  40.0240  -82.6511
##     distance
## 1   4.106137
## 2   4.234920
## 3   4.270197
## 4   5.909011
## 5   6.310790
## 6   6.504242
## 7   7.639623
## 8   7.687720
## 9   8.664212
## 10  9.389591
## 11 10.282003
## 12 11.858973
## 13 12.094496
## 14 12.799033
## 15 13.238469
## 16 13.887609
## 17 14.326951
## 18 14.662096
## 19 14.801971
## 20 16.757176
## 21 16.900247
## 22 17.021110
## 23 17.673463
## 24 18.143413
## 25 21.190424
## 26 21.564691
## 27 22.796070
## 28 26.008132
## 29 29.278406
## 
## $bronx
##             id                        name latitude longitude  distance
## 1  USW00014732    NY NEW YORK LAGUARDIA AP  40.7794  -73.8803  5.616756
## 2  USW00094728    NY NEW YORK CNTRL PK TWR  40.7789  -73.9692  6.167420
## 3  USC00300961                    NY BRONX  40.8369  -73.8494  6.230297
## 4  US1NJBG0018   NJ PALISADES PARK 0.2 WNW  40.8481  -74.0002  7.435652
## 5  US1NJBG0003            NJ TENAFLY 1.3 W  40.9147  -73.9775 11.587163
## 6  US1NYQN0002    NY MIDDLE VILLAGE 0.5 SW  40.7145  -73.8819 12.161951
## 7  USW00094741             NJ TETERBORO AP  40.8500  -74.0614 12.354793
## 8  US1NJBG0001       NJ BERGENFIELD 0.3 SW  40.9213  -74.0020 13.206762
## 9  US1NJBG0012        NJ WOOD RIDGE 0.6 SE  40.8420  -74.0830 13.930427
## 10 US1NJBG0033       NJ WOOD RIDGE 0.6 NNW  40.8536  -74.0943 15.131881
## 11 US1NYWC0009       NY NEW ROCHELLE 1.3 S  40.9040  -73.7770 15.226895
## 12 US1NJBG0013         NJ RUTHERFORD 1.2 N  40.8373  -74.1065 15.809146
## 13 US1NYKN0025          NY BROOKLYN 3.1 NW  40.6846  -73.9867 16.069963
## 14 US1NJBG0031         NJ DEMAREST 0.6 NNW  40.9628  -73.9600 16.230719
## 15 US1NJBG0002   NJ SADDLE BROOK TWP 0.6 E  40.9027  -74.0834 16.534408
## 16 US1NJBG0011   NJ NORTH ARLINGTON 0.7 NE  40.7944  -74.1190 16.988985
## 17 US1NJBG0008 NJ SADDLE BROOK TWP 0.3 NNE  40.9071  -74.0934 17.505118
## 18 USC00286146              NJ NEW MILFORD  40.9611  -74.0158 17.635536
## 19 US1NJBG0015  NJ NORTH ARLINGTON 0.7 WNW  40.7915  -74.1398 18.769309
## 20 US1NJHD0002            NJ KEARNY 1.7 NW  40.7729  -74.1409 19.318492
## 21 US1NJBG0005         NJ WESTWOOD 0.8 ESE  40.9830  -74.0159 19.836072
## 22 US1NJBG0010     NJ RIVER VALE TWP 1.5 S  40.9915  -74.0123 20.587159
## 23 US1NYNS0007        NY FLORAL PARK 0.4 W  40.7230  -73.7110 20.642010
## 24 US1NJHD0001           NJ HARRISON 0.3 N  40.7480  -74.1518 21.094543
## 25 USC00283704                 NJ HARRISON  40.7514  -74.1567 21.338273
## 26 US1NJBG0020          NJ PARAMUS 1.8 NNW  40.9682  -74.0902 21.822558
## 27 USC00302129      NY DOBBS FERRY-ARDSLEY  41.0072  -73.8344 22.023427
## 28 US1NJBG0017        NJ GLEN ROCK 0.7 SSE  40.9511  -74.1183 22.145027
## 29 US1NJES0020         NJ BLOOMFIELD 1.7 S  40.7850  -74.1885 22.932509
## 30 US1NYKN0003          NY BROOKLYN 2.4 SW  40.6194  -73.9859 22.986706
## 31 US1NYWC0005         NY HARRISON 4.1 SSW  40.9639  -73.7232 23.014851
## 32 USC00307587                NY SEA CLIFF  40.8506  -73.6483 23.109762
## 33 US1NJBG0037        NJ GLEN ROCK 0.4 WNW  40.9614  -74.1328 23.815579
## 34 US1NJPS0014        NJ HAWTHORNE 1.0 SSE  40.9436  -74.1523 23.880745
## 35 USC00289832           NJ WOODCLIFF LAKE  41.0139  -74.0425 23.891667
## 36 US1NJES0015        NJ MONTCLAIR 2.2 NNE  40.8565  -74.2004 23.935385
## 37 USW00094789     NY NEW YORK JFK INTL AP  40.6386  -73.7622 24.159135
## 38 US1NJPS0017     NJ WOODLAND PARK 0.1 NW  40.8918  -74.1960 24.547061
## 39 US1NJPS0005          NJ HAWTHORNE 0.4 S  40.9519  -74.1577 24.787058
## 40 US1NJES0011   NJ CEDAR GROVE TWP 0.9 NE  40.8648  -74.2157 25.368252
## 41 USC00305796        NY NY AVE V BROOKLYN  40.5939  -73.9808 25.658206
## 42 US1NJPS0018           NJ PATERSON 2.0 W  40.9163  -74.2005 25.903423
## 43 USW00014734           NJ NEWARK INTL AP  40.6825  -74.1694 25.982975
## 44 US1NJPS0003 NJ LITTLE FALLS TWP 0.2 NNW  40.8788  -74.2205 26.107408
## 45 US1NJPS0012 NJ LITTLE FALLS TWP 0.5 WNW  40.8796  -74.2270 26.658883
## 46 USC00284887             NJ LITTLE FALLS  40.8858  -74.2261 26.764590
## 47 US1NJES0024    NJ CEDAR GROVE TWP 0.4 W  40.8557  -74.2356 26.845270
## 48 US1NYNS0014          NY LYNBROOK 0.3 NW  40.6623  -73.6780 26.891776
## 49 USC00285503             NJ MIDLAND PARK  40.9939  -74.1453 27.062890
## 50 USC00305377                  NY MINEOLA  40.7328  -73.6183 27.191818
## 51 US1NJPS0004      NJ NORTH HALEDON 0.6 N  40.9713  -74.1856 27.953822
## 52 US1NJES0010        NJ VERONA TWP 0.7 SW  40.8255  -74.2531 28.035405
## 53 US1NJES0021       NJ VERONA TWP 0.6 WSW  40.8305  -74.2539 28.119240
## 54 US1NJES0004   NJ NORTH CALDWELL 0.6 SSE  40.8576  -74.2523 28.265572
## 55 US1NJPS0008        NJ WAYNE TWP 1.1 ESE  40.9412  -74.2267 29.094312
## 56 US1NYWC0003     NY WHITE PLAINS 3.1 NNW  41.0639  -73.7722 29.826697
## 57 US1NYNS0009         NY MILL NECK 1.1 SW  40.8704  -73.5717 29.828998
## 58 US1NYRL0005       NY WEST NYACK 1.3 WSW  41.0835  -73.9930 29.934369

Not all the locations have stations nearby. Therefore, I will omit them from the weather data evaluation using the following code.

has_stations <- sapply(stations, function(x) nrow(x) > 0)
outbreak_loc_true <- outbreak_loc %>%  filter(has_stations)
outbreak_loc_true

##              id      file_id latitude longitude year_min   date_min
## 1    pittsburgh   pittsburgh    40.43    -79.98     2002 2002-01-01
## 2        quebec       quebec    46.85    -71.34     2002 2002-01-01
## 3      miyazaki     miyazaki    31.89    131.34     1992 1992-01-01
## 4      pamplona     pamplona    42.81     -1.65     1996 1996-01-01
## 5    rapid city   rapid_city    44.06   -103.22     1995 1995-01-01
## 6     sarpsborg    sarpsborg    59.28     11.08     1995 1995-01-01
## 7        murcia       murcia    37.98     -1.12     1991 1991-01-01
## 8     melbourne    melbourne   -37.86    145.07     1990 1990-01-01
## 9  bovenkarspel bovenkarspel    52.70      5.24     1989 1989-01-01
## 10       london       london    51.52     -0.10     1979 1979-01-01
## 11       sydney       sydney   -33.85    150.93     2006 2006-01-01
## 12     genesee1     genesee1    43.09    -83.63     2004 2004-01-01
## 13     genesee2     genesee2    43.09    -83.63     2005 2005-01-01
## 14     columbus     columbus    39.98    -82.99     2003 2003-01-01
## 15        bronx        bronx    40.82    -73.92     2005 2005-01-01
##    year_max   date_max      onset before_onset
## 1      2012 2012-12-31 2012-08-26   2012-08-12
## 2      2012 2012-12-31 2012-07-18   2012-07-04
## 3      2002 2002-12-31 2002-07-18   2002-07-04
## 4      2006 2006-12-31 2006-06-01   2006-05-18
## 5      2005 2005-12-31 2005-05-26   2005-05-12
## 6      2005 2005-12-31 2005-05-12   2005-04-28
## 7      2001 2001-12-31 2001-06-26   2001-06-12
## 8      2000 2000-12-31 2000-04-17   2000-04-03
## 9      1999 1999-12-31 1999-02-25   1999-02-11
## 10     1989 1989-12-31 1989-01-01   1988-12-18
## 11     2016 2016-12-31 2016-04-25   2016-04-11
## 12     2014 2014-12-31 2014-06-06   2014-05-23
## 13     2015 2015-12-31 2015-05-04   2015-04-20
## 14     2013 2013-12-31 2013-07-09   2013-06-25
## 15     2015 2015-12-31 2015-07-12   2015-06-28

Using the countyweather codes I can gather the data for each station in a loop. The code gathers the weather data for each stations and averages them. Then I saved all the data as rds. files because they take a long time to gather. The data is saved in a folder I created called “weather_files/”

for(i in which(has_stations))
{
  meteo_df <- meteo_pull_monitors(monitors = stations[[i]]$id,
                                  keep_flags = FALSE,
                                  date_min = outbreak_loc$date_min[i],
                                  date_max = outbreak_loc$date_max[i],
                                  var = c("prcp","snow","snwd","tmax","tmin","tavg"))

  coverage_df <- rnoaa::meteo_coverage(meteo_df, verbose = FALSE)
  filtered <- countyweather:::filter_coverage(coverage_df, 0.90)
  good_monitors <- unique(filtered$id)
  filtered_data <- dplyr::filter(meteo_df, id %in% good_monitors)
  averaged <- countyweather:::ave_daily(filtered_data)

  # For metrics that are reported in tenths of units (precipitation
  # and temperature), divide by 10 to get values in degrees Celsius and
  # millimeters
  which_tenth_units <- which(colnames(averaged) %in%
                               c("prcp", "tavg", "tmax", "tmin"))
  averaged[ , which_tenth_units] <- averaged[ , which_tenth_units] / 10

  file_name <- paste0("weather_files/", outbreak_loc$file_id[i], ".rds")
  saveRDS(averaged, file_name)
}

Now that all of the data is gathered and averaged I can plot the data. The loop will go through the files in order which is in alphabetical order. Therefore I must order my outbreak data frame into alphabetical order too. I will rename this data frame as df_stations for plotting.

##              id      file_id latitude longitude year_min   date_min
## 1  bovenkarspel bovenkarspel    52.70      5.24     1989 1989-01-01
## 2         bronx        bronx    40.82    -73.92     2005 2005-01-01
## 3      columbus     columbus    39.98    -82.99     2003 2003-01-01
## 4      genesee1     genesee1    43.09    -83.63     2004 2004-01-01
## 5      genesee2     genesee2    43.09    -83.63     2005 2005-01-01
## 6        london       london    51.52     -0.10     1979 1979-01-01
## 7     melbourne    melbourne   -37.86    145.07     1990 1990-01-01
## 8      miyazaki     miyazaki    31.89    131.34     1992 1992-01-01
## 9        murcia       murcia    37.98     -1.12     1991 1991-01-01
## 10     pamplona     pamplona    42.81     -1.65     1996 1996-01-01
## 11   pittsburgh   pittsburgh    40.43    -79.98     2002 2002-01-01
## 12       quebec       quebec    46.85    -71.34     2002 2002-01-01
## 13   rapid city   rapid_city    44.06   -103.22     1995 1995-01-01
## 14    sarpsborg    sarpsborg    59.28     11.08     1995 1995-01-01
## 15       sydney       sydney   -33.85    150.93     2006 2006-01-01
##    year_max   date_max      onset before_onset
## 1      1999 1999-12-31 1999-02-25   1999-02-11
## 2      2015 2015-12-31 2015-07-12   2015-06-28
## 3      2013 2013-12-31 2013-07-09   2013-06-25
## 4      2014 2014-12-31 2014-06-06   2014-05-23
## 5      2015 2015-12-31 2015-05-04   2015-04-20
## 6      1989 1989-12-31 1989-01-01   1988-12-18
## 7      2000 2000-12-31 2000-04-17   2000-04-03
## 8      2002 2002-12-31 2002-07-18   2002-07-04
## 9      2001 2001-12-31 2001-06-26   2001-06-12
## 10     2006 2006-12-31 2006-06-01   2006-05-18
## 11     2012 2012-12-31 2012-08-26   2012-08-12
## 12     2012 2012-12-31 2012-07-18   2012-07-04
## 13     2005 2005-12-31 2005-05-26   2005-05-12
## 14     2005 2005-12-31 2005-05-12   2005-04-28
## 15     2016 2016-12-31 2016-04-25   2016-04-11

PLOT 1

Outbreak Distribution

This plot is divided by outbreaks in the northern and southern hemisphere. This allows us to see when the outbreaks generally occur in the year.

PLOT 2

10 years for all data

These plots allow for a quick glance into all the weather variables for each location.

## Warning: Removed 5 rows containing missing values (geom_path).

## Warning: Removed 1 rows containing missing values (geom_path).

## Warning: Removed 27 rows containing missing values (geom_path).

PLOT 3

TMAX and TMIN

## Warning: Removed 1100 rows containing missing values (geom_path).

## Warning: Removed 5 rows containing missing values (geom_path).

## Warning: Removed 1 rows containing missing values (geom_path).

## Warning: Removed 55 rows containing missing values (geom_path).

PLOT 4

Precipitation

I also made a loop to plot graphs and histograms of the data with lines indicating each day before the start of the outbreak for a total of 14 days.A plot of the percentiles is also included. The precentile data is saved for a plot of percentiles as shown later.

PLOT 5

TMAX

PLOT 6

TMIN

## Warning: Removed 550 rows containing missing values (geom_path).

## Warning: Removed 550 rows containing non-finite values (stat_bin).

## Warning: Removed 5 rows containing missing values (geom_path).

## Warning: Removed 1587 rows containing non-finite values (stat_bin).

## Warning: Removed 9 rows containing missing values (geom_vline).

## Warning: Removed 9 rows containing missing values (position_stack).

## Warning: Removed 1 rows containing missing values (geom_path).

## Warning: Removed 1595 rows containing non-finite values (stat_bin).

## Warning: Removed 5 rows containing missing values (geom_vline).

## Warning: Removed 5 rows containing missing values (position_stack).

## Warning: Removed 975 rows containing non-finite values (stat_bin).

## Warning: Removed 27 rows containing missing values (geom_path).

## Warning: Removed 27 rows containing non-finite values (stat_bin).

PERCENTILES

Table and Plots

Now I gathered a 2-week seasonal subset data for each weather variable and plotted it in a single table. I saved the table data for each outbreak and plotted them in a facetted plot.s

days_before_onset	TMAX_year	TMAX_seasonal	TMIN_year	TMIN_seasonal	PRCP_year	PRCP_seasonal	outbreak
0	15.690799	54.814815	20.478800	56.296296	71.24720	67.87879	1
1	13.700606	48.148148	15.806173	47.407407	45.55639	39.39394	1
2	14.681281	52.592593	18.257860	51.851852	64.65024	60.60606	1
3	11.479665	41.481482	13.614076	42.222222	87.85163	92.12121	1
4	20.334583	64.444444	25.007211	66.666667	87.12970	90.90909	1
5	15.690799	54.814815	23.622729	63.703704	48.66816	43.03030	1
6	26.593597	74.074074	24.257283	65.925926	59.79587	56.96970	1
7	18.402077	58.518518	18.257860	51.851852	79.33781	80.60606	1
8	9.662532	35.555556	9.085665	33.333333	58.72542	52.72727	1
9	16.094606	57.037037	11.681569	37.037037	72.54170	69.69697	1
10	12.027690	43.703704	13.614076	42.222222	81.22977	83.63636	1
11	6.864725	28.888889	3.345832	14.814815	68.90714	64.84848	1
12	3.663109	13.333333	2.278627	10.370370	29.82325	18.78788	1
13	6.201327	25.925926	5.018748	20.000000	25.44187	15.15152	1
14	7.095472	29.629630	5.797519	22.222222	32.36246	21.81818	1
0	93.104307	67.272727	90.589992	55.757576	37.78940	30.90909	2
1	90.540204	61.818182	87.229276	44.848485	37.78940	30.90909	2
2	84.266866	37.575758	92.805576	67.272727	85.66094	86.66667	2
3	77.346278	19.393939	88.150361	47.878788	88.82250	90.90909	2
4	93.552402	69.090909	99.004232	94.545455	67.68733	63.03030	2
5	92.108539	64.848485	97.684839	89.090909	52.67613	44.84848	2
6	83.619617	34.545454	90.589992	55.757576	37.78940	30.90909	2
7	80.383371	29.090909	85.262634	36.969697	63.05701	58.78788	2
8	72.417227	10.909091	86.706497	41.212121	43.01718	35.15152	2
9	80.084640	27.878788	78.790142	20.000000	46.22853	38.78788	2
10	77.918845	21.212121	85.362211	37.575758	46.65173	39.39394	2
11	84.092606	36.363636	88.399303	49.090909	93.90092	93.33333	2
12	78.317152	23.636364	80.657207	24.242424	46.22853	38.78788	2
13	70.176749	6.060606	74.583022	12.727273	59.69629	54.54545	2
14	65.098332	2.424242	72.840428	6.060606	97.13717	97.57576	2
0	93.504231	72.727273	98.307616	96.969697	92.78248	90.30303	3
1	77.277252	27.878788	87.605774	52.727273	97.56098	95.75758	3
2	78.720757	32.121212	89.447486	60.000000	84.29567	77.57576	3
3	82.901941	40.606061	97.137879	93.333333	80.71180	73.93939	3
4	70.408163	13.939394	91.762071	69.696970	97.06322	94.54545	3
5	71.577900	18.181818	97.735192	95.757576	71.67745	63.03030	3
6	84.693878	46.666667	94.673967	84.848485	39.72125	37.57576	3
7	79.193629	34.545454	92.956695	75.151515	56.09756	49.09091	3
8	76.655052	26.060606	88.899950	56.969697	85.36585	79.39394	3
9	72.025884	18.787879	78.795421	29.696970	95.52016	93.33333	3
10	74.813340	21.818182	79.417621	31.515152	91.18965	87.87879	3
11	83.026381	41.212121	88.402190	54.545454	69.91040	62.42424	3
12	87.730214	54.545454	91.762071	69.696970	89.12394	84.84848	3
13	91.513191	65.454545	91.762071	69.696970	94.99751	92.12121	3
14	97.859632	89.696970	96.689896	90.909091	66.72474	60.00000	3
0	72.573420	52.727273	61.796914	23.030303	36.90891	36.96970	4
1	61.896466	21.212121	65.505227	36.363636	50.14933	51.51515	4
2	73.693380	56.969697	78.546541	60.606061	49.42758	50.90909	4
3	89.646590	80.000000	96.042807	98.181818	73.49428	69.69697	4
4	92.160279	86.060606	88.128422	81.212121	57.41663	57.57576	4
5	86.286710	75.151515	74.763564	55.757576	36.90891	36.96970	4
6	86.112494	73.939394	75.012444	56.969697	36.90891	36.96970	4
7	72.000996	49.696970	67.073171	41.212121	36.90891	36.96970	4
8	65.878546	36.969697	63.787954	29.090909	81.01045	76.96970	4
9	84.146342	71.515152	82.030861	70.909091	87.38178	87.27273	4
10	90.019910	80.606061	86.137382	80.000000	84.64410	83.03030	4
11	86.485814	75.757576	74.539572	54.545454	36.90891	36.96970	4
12	76.704828	60.606061	65.281234	35.151515	36.90891	36.96970	4
13	62.792434	24.242424	61.697362	21.818182	36.90891	36.96970	4
14	64.211050	29.696970	62.667994	24.242424	36.90891	36.96970	4
0	80.358476	95.757576	77.346278	96.969697	68.75778	63.63636	5
1	72.641275	90.909091	61.463779	76.969697	36.71894	34.54545	5
2	67.064974	80.000000	54.742345	60.606061	36.71894	34.54545	5
3	52.128454	47.878788	49.639034	46.060606	52.47697	48.48485	5
4	57.181977	64.848485	52.725915	54.545454	73.83620	67.27273	5
5	54.543191	56.969697	43.241225	30.303030	55.56385	52.12121	5
6	42.618870	26.060606	34.229524	12.121212	36.71894	34.54545	5
7	44.983819	32.121212	41.224795	25.454545	43.63953	42.42424	5
8	42.768235	27.272727	31.715210	7.272727	36.71894	34.54545	5
9	42.544187	24.848485	31.167538	6.060606	36.71894	34.54545	5
10	28.055763	3.030303	21.483694	1.212121	36.71894	34.54545	5
11	24.794623	1.212121	31.814787	8.484849	56.48494	53.93939	5
12	32.636296	6.666667	46.502365	40.606061	53.24869	49.69697	5
13	51.058003	46.666667	52.003983	52.727273	77.22181	69.69697	5
14	58.675629	69.090909	57.729649	68.484848	87.30396	84.84848	5
0	NaN	NaN	NaN	NaN	54.36198	54.46880	6
1	19.630872	18.765091	NaN	NaN	54.36198	54.46880	6
2	NaN	NaN	NaN	NaN	NaN	NaN	6
3	NaN	NaN	43.150967	42.361405	54.36198	54.46880	6
4	NaN	NaN	NaN	NaN	54.36198	54.46880	6
5	NaN	NaN	NaN	NaN	NaN	NaN	6
6	NaN	NaN	65.981078	65.213711	54.36198	54.46880	6
7	34.697987	33.425319	43.150967	42.361405	NaN	NaN	6
8	42.248322	40.772680	NaN	NaN	NaN	NaN	6
9	42.248322	40.772680	NaN	NaN	54.36198	54.46880	6
10	34.697987	33.425319	64.253394	63.478629	54.36198	54.46880	6
11	34.697987	33.425319	44.055944	43.250106	54.36198	54.46880	6
12	NaN	NaN	NaN	NaN	54.36198	54.46880	6
13	35.436242	34.046223	NaN	NaN	64.90885	64.85666	6
14	NaN	NaN	22.459893	21.836648	54.36198	54.46880	6
0	54.131409	46.060606	53.111001	44.848485	83.00149	80.60606	7
1	72.722748	67.878788	84.544550	87.272727	59.35789	63.03030	7
2	71.304131	66.666667	56.943753	48.484848	88.35241	86.66667	7
3	68.292683	65.454545	94.524639	95.757576	85.26630	83.03030	7
4	79.019413	78.787879	90.219014	92.727273	20.35839	18.18182	7
5	85.689398	89.696970	72.772524	72.121212	20.35839	18.18182	7
6	85.988054	90.303030	79.790941	81.818182	32.95172	37.57576	7
7	82.055749	81.818182	74.489796	74.545455	20.35839	18.18182	7
8	87.431558	92.121212	66.177203	63.030303	20.35839	18.18182	7
9	82.503733	82.424242	31.408661	13.939394	27.05326	24.24242	7
10	61.448482	58.181818	41.961175	28.484848	20.35839	18.18182	7
11	58.536585	52.727273	36.635142	22.424242	53.80786	59.39394	7
12	53.583873	44.848485	76.754604	77.575758	98.45694	98.18182	7
13	72.175212	67.272727	52.040816	43.636364	21.30413	18.78788	7
14	74.066700	70.303030	70.930811	69.696970	39.59681	44.24242	7
0	76.093071	16.149068	NaN	NaN	73.02185	78.04878	8
1	86.934288	48.447205	82.872472	27.586207	38.80934	40.24390	8
2	92.815137	64.596273	NaN	NaN	28.56066	29.26829	8
3	83.175658	34.161491	NaN	NaN	66.54107	69.51220	8
4	88.391716	52.173913	97.111019	87.068966	33.83572	34.14634	8
5	90.948606	57.763975	98.060256	93.965517	29.84175	30.48780	8
6	87.241115	49.068323	85.266199	42.241379	14.09194	12.19512	8
7	87.496804	49.689441	83.945522	35.344828	30.06782	31.70732	8
8	97.519816	81.366460	NaN	NaN	14.09194	12.19512	8
9	87.599080	50.310559	97.853900	91.379310	42.65260	45.12195	8
10	88.263871	51.552795	87.825010	51.724138	28.56066	29.26829	8
11	84.709793	41.614907	84.275691	38.793103	NaN	NaN	8
12	83.584761	36.645963	91.209245	64.655172	89.14846	86.58537	8
13	77.371516	19.875776	NaN	NaN	81.91409	81.70732	8
14	77.090258	18.633540	95.542716	81.896552	32.78071	32.92683	8
0	99.850672	100.000000	91.388751	95.757576	83.00149	89.69697	9
1	99.004480	98.181818	84.395222	84.242424	83.00149	89.69697	9
2	98.282728	95.757576	84.494773	85.454545	83.00149	89.69697	9
3	99.825784	99.393939	78.496765	68.484848	83.00149	89.69697	9
4	99.153808	98.787879	73.544052	50.909091	83.00149	89.69697	9
5	93.852663	90.909091	68.367347	30.303030	83.00149	89.69697	9
6	79.442509	60.606061	63.862618	13.939394	83.00149	89.69697	9
7	63.364858	16.969697	72.847188	47.878788	83.00149	89.69697	9
8	62.792434	15.151515	84.046789	83.030303	83.00149	89.69697	9
9	75.684420	49.090909	67.098059	26.060606	83.00149	89.69697	9
10	86.361374	79.393939	90.467894	95.151515	83.00149	89.69697	9
11	96.416127	92.727273	78.496765	68.484848	83.00149	89.69697	9
12	94.848183	92.121212	79.890493	73.333333	83.00149	89.69697	9
13	87.680438	81.818182	69.362867	33.939394	83.00149	89.69697	9
14	63.937282	17.575758	65.355898	18.787879	83.00149	89.69697	9
0	60.129418	30.303030	39.347934	8.484849	59.98009	70.30303	10
1	46.565455	13.333333	51.294176	25.454545	59.98009	70.30303	10
2	45.644599	11.515151	62.020906	51.515152	59.98009	70.30303	10
3	63.937282	36.363636	78.571429	85.454545	59.98009	70.30303	10
4	88.626182	86.666667	80.537581	87.272727	59.98009	70.30303	10
5	92.458935	93.939394	69.437531	67.272727	59.98009	70.30303	10
6	75.385764	58.787879	74.912892	79.393939	59.98009	70.30303	10
7	72.598308	51.515152	37.406670	6.666667	59.98009	70.30303	10
8	56.470881	24.848485	28.297660	1.212121	59.98009	70.30303	10
9	42.508711	6.060606	42.483823	11.515151	59.98009	70.30303	10
10	63.265306	35.151515	66.923843	62.424242	70.05973	77.57576	10
11	85.813838	81.212121	72.125435	75.151515	59.98009	70.30303	10
12	65.778994	40.000000	62.344450	52.727273	59.98009	70.30303	10
13	63.066202	34.545454	54.952713	30.303030	59.98009	70.30303	10
14	65.778994	40.000000	82.205077	89.696970	59.98009	70.30303	10
0	92.906919	83.636364	85.365854	53.333333	26.80438	35.75758	11
1	92.906919	83.636364	82.478845	44.848485	26.80438	35.75758	11
2	91.886511	78.181818	78.596317	32.121212	26.80438	35.75758	11
3	84.420109	50.909091	74.589348	18.787879	48.78049	53.33333	11
4	78.919861	30.909091	71.353907	11.515151	62.51867	62.42424	11
5	75.958188	24.242424	72.448980	15.151515	47.83474	52.72727	11
6	78.944749	31.515152	76.679940	24.848485	62.91687	63.03030	11
7	75.236436	23.636364	76.779492	26.060606	34.71877	43.63636	11
8	72.672972	16.969697	78.496765	31.515152	68.84022	69.09091	11
9	79.417621	33.333333	89.472374	67.272727	69.16376	71.51515	11
10	87.605774	61.818182	83.349925	47.272727	26.80438	35.75758	11
11	80.711797	36.969697	87.580886	63.030303	77.20259	80.00000	11
12	85.788950	55.757576	87.282230	61.818182	66.72474	66.06061	11
13	79.766053	33.939394	83.822797	49.090909	30.76157	40.60606	11
14	67.894475	9.696970	83.051269	46.060606	35.54007	44.84848	11
0	83.897461	32.121212	87.755102	38.787879	29.96516	30.30303	12
1	73.842708	10.909091	97.336984	84.242424	92.53360	89.69697	12
2	91.314087	60.606061	99.128920	93.333333	71.90144	68.48485	12
3	99.253360	96.363636	93.529119	66.666667	99.20358	99.39394	12
4	97.187656	84.848485	97.212544	83.636364	29.96516	30.30303	12
5	99.104032	95.151515	95.843703	77.575758	29.96516	30.30303	12
6	97.038327	83.636364	86.436038	34.545454	29.96516	30.30303	12
7	91.015431	58.787879	73.544052	2.424242	29.96516	30.30303	12
8	86.460926	41.212121	80.338477	16.969697	29.96516	30.30303	12
9	85.042310	38.181818	82.852165	23.636364	29.96516	30.30303	12
10	79.168741	19.393939	89.024390	43.636364	29.96516	30.30303	12
11	96.042807	80.000000	98.158288	87.878788	29.96516	30.30303	12
12	93.056247	67.878788	93.728223	67.878788	29.96516	30.30303	12
13	90.841215	56.969697	94.300647	70.909091	32.27974	30.90909	12
14	81.981085	27.878788	93.529119	66.666667	76.92882	72.72727	12
0	52.065704	30.909091	61.523146	43.030303	72.17521	56.96970	13
1	52.090592	31.515152	63.339970	49.090909	97.73519	95.15152	13
2	64.410154	61.212121	71.378795	77.575758	90.89099	83.63636	13
3	73.668492	86.060606	76.356396	90.909091	51.94126	33.33333	13
4	76.331508	92.121212	70.109507	75.757576	51.94126	33.33333	13
5	79.965157	95.151515	81.159781	96.969697	51.94126	33.33333	13
6	86.162270	98.181818	82.105525	97.575758	51.94126	33.33333	13
7	84.619214	97.575758	73.444500	86.060606	51.94126	33.33333	13
8	70.681931	79.393939	73.295172	85.454545	57.41663	37.57576	13
9	70.532603	77.575758	72.672972	84.242424	71.92633	55.75758	13
10	75.136884	88.484848	72.025884	80.606061	51.94126	33.33333	13
11	66.898955	68.484848	45.943255	8.484849	51.94126	33.33333	13
12	45.196615	20.000000	44.997511	7.272727	51.94126	33.33333	13
13	39.696366	9.696970	42.259831	5.454546	72.17521	56.96970	13
14	26.903932	1.818182	38.352414	3.030303	99.45246	97.57576	13
0	73.184358	72.500000	40.354913	14.166667	31.85665	41.21212	14
1	64.344397	65.833333	46.171541	25.833333	41.09009	45.45455	14
2	50.607953	31.666667	46.565889	27.500000	67.02339	66.66667	14
3	50.607953	31.666667	44.134078	21.666667	72.32454	73.93939	14
4	49.030562	26.666667	44.134078	21.666667	74.51468	75.75758	14
5	52.152481	37.500000	48.209004	35.833333	24.71379	36.36364	14
6	58.724942	52.500000	46.335853	26.666667	24.71379	36.36364	14
7	52.152481	37.500000	34.538285	6.666667	49.22847	50.30303	14
8	52.875452	38.333333	65.856063	87.500000	24.71379	36.36364	14
9	58.724942	52.500000	63.851462	83.333333	64.03683	64.24242	14
10	50.607953	31.666667	59.710812	75.000000	76.18218	78.78788	14
11	62.668419	60.000000	59.710812	75.000000	77.25236	80.60606	14
12	47.321722	20.833333	60.893855	76.666667	61.07516	60.00000	14
13	57.837660	50.000000	52.875452	50.833333	85.26630	89.69697	14
14	64.344397	65.833333	42.885311	18.333333	24.71379	36.36364	14
0	52.791109	53.333333	41.414141	21.818182	46.59091	38.18182	15
1	46.956302	40.000000	59.772727	72.727273	87.65152	81.21212	15
2	32.306138	17.575758	63.358586	81.212121	81.11111	73.93939	15
3	69.133620	85.454545	59.545454	71.515152	57.67677	46.06061	15
4	77.721647	92.121212	62.095960	77.575758	26.86869	18.78788	15
5	70.093458	86.666667	59.848485	73.333333	71.36364	62.42424	15
6	58.651175	65.454545	65.984848	85.454545	89.19192	83.03030	15
7	54.079313	55.151515	56.363636	61.818182	80.05051	71.51515	15
8	50.543066	48.484848	80.429293	96.969697	64.77273	53.33333	15
9	81.156858	94.545455	55.075758	55.757576	49.26768	40.60606	15
10	76.483961	90.303030	58.813131	69.696970	64.52020	52.72727	15
11	53.447840	53.939394	54.898990	55.151515	66.99495	54.54545	15
12	57.438747	62.424242	56.565657	62.424242	66.43939	53.93939	15
13	49.078050	44.848485	74.015152	94.545455	79.06566	70.30303	15
14	70.346047	87.272727	43.787879	27.272727	20.83333	14.54545	15

## Warning: Removed 8 rows containing missing values (position_stack).

## Warning: Removed 8 rows containing missing values (position_stack).

## Warning: Removed 14 rows containing missing values (position_stack).

## Warning: Removed 14 rows containing missing values (position_stack).

## Warning: Removed 5 rows containing missing values (position_stack).

## Warning: Removed 5 rows containing missing values (position_stack).

Legionnaires’ Disease Outbreaks

Holly Kessler

August 10, 2016